{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Задача - ответить на вопрос есть ли связь между жёсткостью воды и средней годовой смертностью?\n",
    "\n",
    "- Построить точечный график\n",
    "- Рассчитать коэффициенты корреляции Пирсона и Спирмена\n",
    "- Построить модель линейной регрессии\n",
    "- Рассчитать коэффициент детерминации\n",
    "- Вывести график остатков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>town</th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South</td>\n",
       "      <td>Bath</td>\n",
       "      <td>1247</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North</td>\n",
       "      <td>Birkenhead</td>\n",
       "      <td>1668</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>South</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>1466</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>North</td>\n",
       "      <td>Blackburn</td>\n",
       "      <td>1800</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>North</td>\n",
       "      <td>Blackpool</td>\n",
       "      <td>1609</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  location        town  mortality  hardness\n",
       "1    South        Bath       1247       105\n",
       "2    North  Birkenhead       1668        17\n",
       "3    South  Birmingham       1466         5\n",
       "4    North   Blackburn       1800        14\n",
       "5    North   Blackpool       1609        18"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_water = pd.read_csv('water.csv', index_col=0)\n",
    "df_water[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Качество воды в зависимости от уровня жесткости\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"water-ppm.png\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Качество воды в зависимости от уровня жесткости')\n",
    "display(Image(url='water-ppm.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим зависимость уровня жесткости воды от уровня смертности с помощью точечной диаграммы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df_water, x='hardness', y='mortality')\n",
    "plt.title(\"Зависимость уровня смертности от жесткости воды\")\n",
    "plt.xlabel('Уровень смертности')\n",
    "plt.ylabel('Жесткость воды')\n",
    "plt.yticks(np.linspace(0, 2000, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На графике можно наблюдать распределение, похожее на линейное обратное распределение.\n",
    "\n",
    "Посмотрим на базовые статистические меры и проверим имеются ли выбросы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1524.147541</td>\n",
       "      <td>47.180328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>187.668754</td>\n",
       "      <td>38.093966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1096.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1379.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1555.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1668.000000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1987.000000</td>\n",
       "      <td>138.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mortality    hardness\n",
       "count    61.000000   61.000000\n",
       "mean   1524.147541   47.180328\n",
       "std     187.668754   38.093966\n",
       "min    1096.000000    5.000000\n",
       "25%    1379.000000   14.000000\n",
       "50%    1555.000000   39.000000\n",
       "75%    1668.000000   75.000000\n",
       "max    1987.000000  138.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_water.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Стоит отметить достаточно высокое стандартное отклонение, это значит мы имеем дело с очень разнообразным кол-вом значений. По максимальному и минимальному значению уровня жесткости можно сказать, что в целом люди в городах пьют качественную воду за исключением 25-30% людей, которые пьют воду с жесткостью менее 20-30 ppm. Считается, что дистиллированную или близкую к ней воду пить вредно, так как отсутствуют минералы, необходимые для организма человека, а так же чистая вода вымывает дополнительно и уже имеющиеся минералы из организма человека.\n",
    "\n",
    "Посмотрим гистрограммы распределения значений величин уровня смертности и жесткости воды."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df_water.mortality)\n",
    "plt.title('Гистрогамма распределения значений уровня смертности')\n",
    "plt.xlabel('Уровень смертности')\n",
    "plt.ylabel('Кол-во значений')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df_water.hardness)\n",
    "plt.title('Плотность распределения значений уровня жесткости воды')\n",
    "plt.xlabel('Уровень жесткости воды')\n",
    "plt.ylabel('Кол-во значений')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно большинство значений уровня жесткости воды попадает в уровень до 20, это означает, что во многих городах течет вода из под крана почти на уровне дистилированной, что тоже вызывает сомнение. Предполагаем, что вода жесткости ниже 40 может наносить относительный вред человеку, особенно дистиллированная вода с ppm <=5. \n",
    "\n",
    "На нашем изображении как раз питьевая вода хорошего качества стартует примерно от 40 ppm. Также исходя из максимального размера жесткости в 138 ppm видно, что в верхней части данных у нас вода считается хорошего качества и даже не жесткой, а на уровне воды из артезианской скважины с угольной фильтрацией и по идее не должна наносить вред человеку. \n",
    "\n",
    "Также значения до 20 можно назвать выбросами, удалять из выборки их нельзя, имеет смысл рассмотреть статистические данные для этой группы отдельно. Есть подозрение, что в городах с такими значениями люди пьют воду не из водопровода. Если мы уберем этот выброс, то у нас получатся данные примерно похожие на нормальное распределение и будет примерно похоже, что в этой ситуации люди пьют воду из-под крана.\n",
    "\n",
    "Рассчитаем коэффициенты корреляции Спирсона и Спирмена. Уровени смертности и жесткости воды можно считать непрерывными величинами, поэтому метод Спирсона будет работать неплохо, но ему может помешать аномальные значения жесткости воды до 20 уровня. Но таких значений у нас почти половина в датасете, поэтому предположительно влияние будет не сильно большое. А на коэффициент корреляции Спирмана выбросы не должны сильно повлиять."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mortality</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.654849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hardness</th>\n",
       "      <td>-0.654849</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mortality  hardness\n",
       "mortality   1.000000 -0.654849\n",
       "hardness   -0.654849  1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Коэффициент корреляции Пирсона\n",
    "df_water[['mortality', 'hardness']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mortality</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.631665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hardness</th>\n",
       "      <td>-0.631665</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mortality  hardness\n",
       "mortality   1.000000 -0.631665\n",
       "hardness   -0.631665  1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Коэффициент корреляции Спирмана\n",
    "df_water[['mortality', 'hardness']].corr(method='spearman')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно из коэффициентов зависимость смертности от уровня жесткости воды достаточная, но не сильно огромная. У нас получились отрицательные коэфициенты, значит зависимость обратная. Чем выше значение жесткости воды, тем ниже смертность. \n",
    "\n",
    "А что произойдет, если мы уберем из датасета города, в которых аномально чистая вода, и заново пересчитаем коэффициенты?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x152282044a8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df_water.hardness[df_water.hardness>20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mortality</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.495797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hardness</th>\n",
       "      <td>-0.495797</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mortality  hardness\n",
       "mortality   1.000000 -0.495797\n",
       "hardness   -0.495797  1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Коэффициент корреляции Пирсона\n",
    "df_water[df_water.hardness>20][['mortality', 'hardness']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mortality</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.499388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hardness</th>\n",
       "      <td>-0.499388</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mortality  hardness\n",
       "mortality   1.000000 -0.499388\n",
       "hardness   -0.499388  1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Коэффициент корреляции Спирмана\n",
    "df_water[df_water.hardness>20][['mortality', 'hardness']].corr(method='spearman')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видим после удаления аномальных значений жесткости воды у нас произошло снижение корреляционной зависимости.\n",
    "\n",
    "Посмотрим как изменится точечная диаграмма после удаления значений жесткости воды >20:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df_water[df_water.hardness>20], x='hardness', y='mortality')\n",
    "plt.title(\"Зависимость уровня смертности от жесткости воды\")\n",
    "plt.xlabel('Уровень смертности')\n",
    "plt.ylabel('Жесткость воды')\n",
    "plt.yticks(np.linspace(0, 2000, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно график уже выглядит так, как будто в данных можно првоести прямую линию с постоянным значением y, а это уже говорит о том, что уровень смертности мало зависит от жесткости воды в случае значений жесткости от 20 до 138.\n",
    "\n",
    "Построим линейную регресионную модель на основе полного датасета."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициент а: -3.457219507521319\n",
      "Коэффициент b: 1694.3947494490756\n",
      "Значения предсказаний уровня смертности:\n",
      "[1632.16479831 1524.99099358 1621.79313979 1459.30382294 1497.33323752\n",
      " 1217.29845741 1511.16211555 1625.2503593  1272.61396953 1566.47762767\n",
      " 1403.98831082 1604.50704225 1673.6514324  1639.07923733 1379.78777426\n",
      " 1645.99367634 1652.90811536 1645.99367634 1649.45089585 1486.961579\n",
      " 1324.47226214 1666.73699339 1666.73699339 1635.62201782 1369.41611574\n",
      " 1677.10865191 1521.53377407 1383.24499377 1601.04982275 1663.27977388\n",
      " 1448.93216442 1442.0177254  1666.73699339 1272.61396953 1448.93216442\n",
      " 1542.27709112 1424.73162786 1649.45089585 1362.50167673 1642.53645684\n",
      " 1649.45089585 1677.10865191]\n",
      "Коэффициент детерминации:  0.389489875734762\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "mortality_test, mortality_train, hardness_test, hardness_train = train_test_split(df_water['mortality'],\n",
    "                                                                                  df_water[['hardness']],\n",
    "                                                                                  test_size=0.3,\n",
    "                                                                                  random_state=42)\n",
    "model.fit(hardness_train, mortality_train)\n",
    "print('Коэффициент а:', model.coef_[0])\n",
    "print('Коэффициент b:', model.intercept_)\n",
    "mortality_predict = model.predict(hardness_test)\n",
    "print('Значения предсказаний уровня смертности:')\n",
    "print(mortality_predict)\n",
    "print('Коэффициент детерминации: ', model.score(hardness_test, mortality_test))\n",
    "\n",
    "plt.scatter(hardness_test, mortality_test)\n",
    "plt.plot(hardness_test, mortality_predict, c='r')\n",
    "plt.title('Регресионная модель предсказания уровня смертность от уровня жесткости воды')\n",
    "plt.xlabel('Уровень жесткости воды')\n",
    "plt.ylabel('Уровень смертности')\n",
    "plt.yticks(np.linspace(0, 2000, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видим коэффициент детерминации по сравнению моделью отклонения от среднего достаточно невысока. \n",
    "\n",
    "Попробуем построить модель без аномальных значений уровня жесткости воды."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициент а: -3.285276317094948\n",
      "Коэффициент b: 1699.2808314316894\n",
      "Значения предсказаний уровня смертности:\n",
      "[1433.17344975 1354.32681814 1630.29002877 1512.02008136 1577.7256077\n",
      " 1262.33908126 1443.0292787  1554.72867348 1443.0292787  1538.30229189\n",
      " 1423.3176208  1298.47712075 1459.45566028 1525.16118663 1367.46792341\n",
      " 1452.88510765 1630.29002877 1403.60596289 1505.44952872 1475.88204187\n",
      " 1383.89430499 1610.57837087 1571.15505506 1245.91269967 1466.02621292]\n",
      "Коэффициент детерминации:  0.15294338454788592\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "mortality_test, mortality_train, hardness_test, hardness_train = train_test_split(df_water[df_water.hardness>20]['mortality'],\n",
    "                                                                                  df_water[df_water.hardness>20][['hardness']],\n",
    "                                                                                  test_size=0.3,\n",
    "                                                                                  random_state=42)\n",
    "model.fit(hardness_train, mortality_train)\n",
    "print('Коэффициент а:', model.coef_[0])\n",
    "print('Коэффициент b:', model.intercept_)\n",
    "mortality_predict = model.predict(hardness_test)\n",
    "print('Значения предсказаний уровня смертности:')\n",
    "print(mortality_predict)\n",
    "print('Коэффициент детерминации: ', model.score(hardness_test, mortality_test))\n",
    "\n",
    "plt.scatter(hardness_test, mortality_test)\n",
    "plt.plot(hardness_test, mortality_predict, c='r')\n",
    "plt.title('Регресионная модель предсказания уровня смертность от уровня жесткости воды')\n",
    "plt.xlabel('Уровень жесткости воды')\n",
    "plt.ylabel('Уровень смертности')\n",
    "plt.yticks(np.linspace(0, 2000, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видим коэффициент детерминации уменьшился почти в 3 раза после отсечки городов с уровнем жесткости воды <= 20 и составляет всего 0.15. \n",
    "\n",
    "Отсюда можно предположить, что если и есть зависимость уровня смертности от уровня жесткости воды, то ее надо рассматривать с иными характеристиками вместе. Например, чем питается человек в течение дня, какие витамины он пьет или не пьет, а так же уровень загрязнения воздуха и многое другое. У человека может течь из под крана очень жесткая вода, но с помощью угольного фильтра человек может ее очистить. А человек, который пьет дистиллирвоанную воду, может принимать витамины и минералы, и на него это не повлияет. Можно пить витамины и хорошую воду, но люди в целом будут быстро умирать от ядовитых выбросов завода в черте их города. Поэтому по этим данным сложно утверждать, что зависимость реально есть, но и отрицать ее невозможно.\n",
    "\n",
    "Построим график остатков."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Разница между предсказанием и реальным значением')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hardness_test['mortality_ost'] = np.abs(mortality_predict-mortality_test)\n",
    "hardness_test.sort_values(by='hardness').plot('hardness', 'mortality_ost')\n",
    "plt.title('График остатков от предсказания уровня смертности')\n",
    "plt.xlabel('Уровень жесткости воды')\n",
    "plt.ylabel('Разница между предсказанием и реальным значением')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.Сохраняется ли аналогичная зависимость для северных и южных городов по отдельности?\n",
    "\n",
    "a.Разделить данные на 2 группы  \n",
    "b.Повторить аналогичные шаги из пункта 1 для каждой группы по отдельности  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1247</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1466</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1299</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1359</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1392</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mortality  hardness\n",
       "1        1247       105\n",
       "3        1466         5\n",
       "8        1299        78\n",
       "10       1359        84\n",
       "11       1392        73"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_water_south = df_water[df_water.location == 'South'][['mortality', 'hardness']]\n",
    "df_water_south[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1668</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1800</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1609</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1558</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1807</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mortality  hardness\n",
       "2       1668        17\n",
       "4       1800        14\n",
       "5       1609        18\n",
       "6       1558        10\n",
       "7       1807        15"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_water_north = df_water[df_water.location == 'North'][['mortality', 'hardness']]\n",
    "df_water_north[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на базовые статистические метрики в разрезе южны и северных городов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1376.807692</td>\n",
       "      <td>69.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>140.269175</td>\n",
       "      <td>40.360682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1096.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1259.250000</td>\n",
       "      <td>40.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1364.000000</td>\n",
       "      <td>75.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1485.750000</td>\n",
       "      <td>99.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1627.000000</td>\n",
       "      <td>138.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mortality    hardness\n",
       "count    26.000000   26.000000\n",
       "mean   1376.807692   69.769231\n",
       "std     140.269175   40.360682\n",
       "min    1096.000000    5.000000\n",
       "25%    1259.250000   40.250000\n",
       "50%    1364.000000   75.500000\n",
       "75%    1485.750000   99.750000\n",
       "max    1627.000000  138.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Южные города\n",
    "df_water_south.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1633.600000</td>\n",
       "      <td>30.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>136.936911</td>\n",
       "      <td>26.134494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1378.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1557.500000</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1637.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1718.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1987.000000</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mortality   hardness\n",
       "count    35.000000  35.000000\n",
       "mean   1633.600000  30.400000\n",
       "std     136.936911  26.134494\n",
       "min    1378.000000   6.000000\n",
       "25%    1557.500000  12.500000\n",
       "50%    1637.000000  17.000000\n",
       "75%    1718.000000  44.000000\n",
       "max    1987.000000  94.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Северные города\n",
    "df_water_north.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По базовым метрикам видно, что в южных городах люди пьют более жесткую воду, но она в целом является качественной. А в серверных городах не менее 50% людей пьют воду с жесткостью не более 17 ppm, назвать такую воду качественной уже нельзя. Из этого можно сделать вывод, что в северных городах должен быть уровень смертности повыше, чем в южных, если вода сжесткостью менее 20 ppm действительно влияет отрицательно на организм человека."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df_water_south, x='hardness', y='mortality')\n",
    "plt.title(\"Зависимость уровня смертности от жесткости воды в южных городах\")\n",
    "plt.xlabel('Уровень смертности')\n",
    "plt.ylabel('Жесткость воды')\n",
    "plt.yticks(np.linspace(0, 2000, 10))\n",
    "plt.show()\n",
    "\n",
    "sns.scatterplot(data=df_water_north, x='hardness', y='mortality')\n",
    "plt.title(\"Зависимость уровня смертности от жесткости воды в северных городах\")\n",
    "plt.xlabel('Уровень смертности')\n",
    "plt.ylabel('Жесткость воды')\n",
    "plt.yticks(np.linspace(0, 2000, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно из точечных графиков в северных городах действительно смертность немного выше. При этом если рассматривать уровни жесткости воды ppm до 20 на обоих графиках, то по какой-то причине люди пьющие почти дистиллированную воду в южных городах имеют немного ниже смертность. Уже можно делать предположения, что на смертность могут влиять другие факторы и нельзя рассматривать только воду. Так как и там, и там есть города с никим значением ppm воды и уровень смертности в них различается.\n",
    "\n",
    "Рассмотрим гистограммы значений уровня смертности и жесткости воды в южных и северных городах."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df_water_south.mortality)\n",
    "plt.title('Гистрогамма распределения значений уровня смертности в южных городах')\n",
    "plt.xlabel('Уровень смертности')\n",
    "plt.ylabel('Кол-во значений')\n",
    "plt.xticks(np.linspace(1000, 2000, 10))\n",
    "plt.show()\n",
    "\n",
    "sns.histplot(df_water_north.mortality)\n",
    "plt.title('Гистрогамма распределения значений уровня смертности в северных городах')\n",
    "plt.xlabel('Уровень смертности')\n",
    "plt.ylabel('Кол-во значений')\n",
    "plt.xticks(np.linspace(1000, 2000, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно действительно в серверных городах уровень смертности выше, график смещен в правую часть."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df_water_south.hardness)\n",
    "plt.title('Гистрогамма распределения значений уровня жесткости воды в южных городах')\n",
    "plt.xlabel('Уровень жесткости воды')\n",
    "plt.ylabel('Кол-во значений')\n",
    "plt.xticks(np.linspace(0, 150, 10))\n",
    "plt.show()\n",
    "\n",
    "sns.histplot(df_water_north.hardness)\n",
    "plt.title('Гистрогамма распределения значений уровня жесткости воды в северных городах')\n",
    "plt.xlabel('Уровень жесткости воды')\n",
    "plt.ylabel('Кол-во значений')\n",
    "plt.xticks(np.linspace(0, 150, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно из графиков дейсвтительно в северных городах люди в большинстве своём пьют воду крайне бедную на минералы. В связи с этим сдвиг значений в гистограмме по уровню смертности у северных городов в сторону увеличения можно объяснить некачественной водой."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mortality</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.602153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hardness</th>\n",
       "      <td>-0.602153</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mortality  hardness\n",
       "mortality   1.000000 -0.602153\n",
       "hardness   -0.602153  1.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Коэффициент корреляции Пирсона для южных городов\n",
    "df_water_south[['mortality', 'hardness']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mortality</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.595723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hardness</th>\n",
       "      <td>-0.595723</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mortality  hardness\n",
       "mortality   1.000000 -0.595723\n",
       "hardness   -0.595723  1.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Коэффициент корреляции Спирмана для южных городов\n",
    "df_water_south[['mortality', 'hardness']].corr(method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mortality</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.368598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hardness</th>\n",
       "      <td>-0.368598</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mortality  hardness\n",
       "mortality   1.000000 -0.368598\n",
       "hardness   -0.368598  1.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Коэффициент корреляции Пирсона для северных городов\n",
    "df_water_north[['mortality', 'hardness']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mortality</th>\n",
       "      <th>hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mortality</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.404208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hardness</th>\n",
       "      <td>-0.404208</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mortality  hardness\n",
       "mortality   1.000000 -0.404208\n",
       "hardness   -0.404208  1.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Коэффициент корреляции Спирмана для северных городов\n",
    "df_water_north[['mortality', 'hardness']].corr(method='spearman')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ранее мы получали значения коэфициентов около 0.49. Сейчас видно, что коэфициенты для южных городов  подросли на 0.1, а для северных городов наоборот упали примерно на 0.1. В связи с этим можно предположить, что оценивать данные смертности только по жесткости воды - сомнительная затея. Вроде бы у северных городов явно смертность повыше, пьют они менее качестенную воду, но коэффициент коррелиции заметно снизился."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициент а: -0.5392432068268586\n",
      "Коэффициент b: 1403.834970806198\n",
      "Значения предсказаний уровня смертности:\n",
      "[1375.25508084 1392.51086346 1361.77400067 1396.28556591 1346.13594768\n",
      " 1358.53854143 1364.47021671 1396.82480912 1355.30308219 1371.4803784\n",
      " 1349.37140692 1360.15627105 1376.87281046 1352.06762295 1329.41940826\n",
      " 1332.1156243  1367.16643274 1361.77400067]\n",
      "Коэффициент детерминации:  0.1703346332829898\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "mortality_test, mortality_train, hardness_test, hardness_train = train_test_split(df_water_south['mortality'],\n",
    "                                                                                  df_water_south[['hardness']],\n",
    "                                                                                  test_size=0.3,\n",
    "                                                                                  random_state=42)\n",
    "model.fit(hardness_train, mortality_train)\n",
    "print('Коэффициент а:', model.coef_[0])\n",
    "print('Коэффициент b:', model.intercept_)\n",
    "mortality_predict = model.predict(hardness_test)\n",
    "print('Значения предсказаний уровня смертности:')\n",
    "print(mortality_predict)\n",
    "print('Коэффициент детерминации: ', model.score(hardness_test, mortality_test))\n",
    "\n",
    "plt.scatter(hardness_test, mortality_test)\n",
    "plt.plot(hardness_test, mortality_predict, c='r')\n",
    "plt.title('Регресионная модель предсказания уровня смертность от уровня жесткости воды в южных городах')\n",
    "plt.xlabel('Уровень жесткости воды')\n",
    "plt.ylabel('Уровень смертности')\n",
    "plt.yticks(np.linspace(0, 2000, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hardness_test['mortality_ost'] = np.abs(mortality_predict-mortality_test)\n",
    "hardness_test.sort_values(by='hardness').plot('hardness', 'mortality_ost')\n",
    "plt.title('График остатков от предсказания уровня смертности')\n",
    "plt.xlabel('Уровень жесткости воды')\n",
    "plt.ylabel('Разница между предсказанием и реальным значением')\n",
    "plt.legend(labels=['Остатки предсказания'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициент а: -1.4700145863721603\n",
      "Коэффициент b: 1693.4030839758284\n",
      "Значения предсказаний уровня смертности:\n",
      "[1664.00279225 1668.41283601 1671.35286518 1681.64296728 1672.82287977\n",
      " 1678.70293811 1555.22171286 1672.82287977 1666.94282142 1589.03204834\n",
      " 1678.70293811 1674.29289435 1674.29289435 1609.61225255 1628.72244218\n",
      " 1684.58299646 1655.18270473 1621.37236924 1675.76290894 1653.71269014\n",
      " 1589.03204834 1664.00279225 1559.63175662 1583.15199   ]\n",
      "Коэффициент детерминации:  0.14900378756159305\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "mortality_test, mortality_train, hardness_test, hardness_train = train_test_split(df_water_north['mortality'],\n",
    "                                                                                  df_water_north[['hardness']],\n",
    "                                                                                  test_size=0.3,\n",
    "                                                                                  random_state=42)\n",
    "model.fit(hardness_train, mortality_train)\n",
    "print('Коэффициент а:', model.coef_[0])\n",
    "print('Коэффициент b:', model.intercept_)\n",
    "mortality_predict = model.predict(hardness_test)\n",
    "print('Значения предсказаний уровня смертности:')\n",
    "print(mortality_predict)\n",
    "print('Коэффициент детерминации: ', model.score(hardness_test, mortality_test))\n",
    "\n",
    "plt.scatter(hardness_test, mortality_test)\n",
    "plt.plot(hardness_test, mortality_predict, c='r')\n",
    "plt.title('Регресионная модель предсказания уровня смертность от уровня жесткости воды в северных городах')\n",
    "plt.xlabel('Уровень жесткости воды')\n",
    "plt.ylabel('Уровень смертности')\n",
    "plt.yticks(np.linspace(0, 2000, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hardness_test['mortality_ost'] = np.abs(mortality_predict-mortality_test)\n",
    "hardness_test.sort_values(by='hardness').plot('hardness', 'mortality_ost')\n",
    "plt.title('График остатков от предсказания уровня смертности')\n",
    "plt.xlabel('Уровень жесткости воды')\n",
    "plt.ylabel('Разница между предсказанием и реальным значением')\n",
    "plt.legend(labels=['Остатки предсказания'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно из полученных моделей коэфициент детерминации крайне низок и даже хуже, чем в полной выборке без деления. А линейная регрессия больше напоминает прямую с постоянным значением уровня смертности.\n",
    "\n",
    "# ВЫВОДЫ\n",
    "\n",
    "1. Нельзя однозначно сказать повлиял ли уровнь жесткости воды на уровень смертности в городах, жесткость воды следует рассматривать с другими признаками, влияющими на качество жизни человека.\n",
    "\n",
    "2. Повышенная смертность в северных городах по сравнению с южными может объясняться другими причинами, например, выбросами с заводов.\n",
    "\n",
    "3. Нельзя утверждать от отсутствии влияния жесткости воды на уровень смертности."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
